**7. Conclusion and prospects**

The sparseness in crowdfunding platform Kickstarter is more than 99% [35]. With such a high sparseness, cosine-based CF obtains poor recommendation performance. Therefore, we use the bipartite graph-based network structure to describe users' behaviors and use PersonalRank to calculate the distance between campaigns and users to directly produce recommendation lists. Next, we integrate bipartite graph model and CF algorithm, and the correlation among the items set (the users set) is obtained by PersonalRank as the measurement of interest similarity. Experimental results show that recommender based on bipartite graph model achieves better performance on a sparse dataset. This paper proposes a method to solve the problem of sparse data, providing a new idea for generating recommendation list in crowdfunding platforms.

Directions for future works are as follows. (1) In terms of bipartite graph model, PersonalRank is not the only algorithm, while other network algorithms are

*A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data DOI: http://dx.doi.org/10.5772/intechopen.92781*

applicable to calculate the node similarity, such as SimRank [36]. Other graph models could be applied to recommendation for crowdfunding campaigns in the future. (2) Due to computing complexity, all of CF algorithms used in this paper are item-based, rather than user-based. However, we have to use user-based recommender in some cases. For example, when a new user enters the system, user-based method is more suitable in recommendation. Future research could make a comparison with user-based recommender algorithms. (3) The datasets are all from Kickstarter, but there are other crowdfunding platforms, such as Indiegogo [37]. Research could use other crowdfunding platforms to verify the applicability of bipartite graph model. (4) Based on the data from the crowdfunding platform, we have verified the usefulness of bipartite graph model. However, not all the information in crowdfunding communities is used. For example, some research found the home bias is a common phenomenon in investment [38], that is, offline relationships between founders and investors may have already been established, such as friends, classmates, acquaintances, colleagues, etc. Consequently, there is a psychological and cultural convergence between founders and investors, and the physical distance is relatively close. Therefore, in personalized recommender, the physical distance in graph model could be considered, and the physical distance between users could be modeled into binary graph model to improve the performance of recommender.
